Predicting location of a mobile user

ABSTRACT

Disclosed are various embodiments for predicting a future location of a mobile user. A recent location of a mobile user is received. Past location data for the mobile user is retrieved from storage. A future location of the mobile user is predicted based at least in part on the recent location and on the past location data. The prediction is provided in response to a query or by subscription.

BACKGROUND

At the present time, various package delivery services provide differentoptions for delivering a package to a user. The options include, forexample, next day or overnight delivery, and may even include deliverywithin a few hours if the shipper and recipient are located in the samegeographic area. Existing delivery services rely on delivery to aparticular location rather than to a particular person. That is,existing delivery services use a location such as home or office as aproxy for the actual location of the delivery target. However, asignificant delay in getting the package to the recipient can occur whenthe package is delivered to one location (e.g., recipient's home) butthe recipient is not at that location and does not return for severalhours or even days.

BRIEF DESCRIPTION OF THE DRAWINGS

Many aspects of the present disclosure can be better understood withreference to the following drawings. The components in the drawings arenot necessarily to scale, emphasis instead being placed upon clearlyillustrating the principles of the disclosure. Moreover, in thedrawings, like reference numerals designate corresponding partsthroughout the several views.

FIG. 1 is a drawing of a networked environment according to variousembodiments of the present disclosure.

FIG. 2 is a flowchart illustrating one example of functionalityimplemented as portions of a location predictor application executed ina computing device in the networked environment of FIG. 1 according tovarious embodiments of the present disclosure.

FIG. 3 is a flowchart illustrating another example of functionalityimplemented as portions of a location predictor application executed ina computing device in the networked environment of FIG. 1 according tovarious embodiments of the present disclosure.

FIG. 4 is a diagram illustrating interactions between various componentsof the networked environment of FIG. 1 according to various embodimentsof the present disclosure.

FIG. 5 is a schematic block diagram that provides one exampleillustration of a computing device employed in the networked environmentof FIG. 1 according to various embodiments of the present disclosure.

DETAILED DESCRIPTION

The present disclosure relates to tracking a recent location of a mobileuser and predicting the mobile user's future location based on therecent location and contextual location data for this user. Thecontextual location data may include various types of information aboutthe mobile user, such as a calendar, a trip itinerary, and/or locationinformation about other users who act as a proxy for the mobile user.The mobile user's predicted location may be used to provide convenientdelivery of a package to the mobile user as he moves from one locationto another. The package shipper and/or mobile user may be chargeddifferent prices for various delivery modes, levels of convenience, etc.

With reference to FIG. 1, shown is a networked environment 100 accordingto various embodiments. The networked environment 100 includes one ormore computing devices 103 and one or more computing devices 106 in datacommunication with one or more client devices 109 by way of a network112. The computing devices 103 and 106 are also in data communicationwith one or more client devices 115 by way of a network 118. Thenetworks 112 and 118 include, for example, the Internet, intranets,extranets, wide area networks (WANs), local area networks (LANs), wirednetworks, wireless networks, or other suitable networks, etc., or anycombination of two or more such networks.

The computing device 103 or 106 may comprise, for example, a servercomputer or any other system providing computing capability.Alternatively, a plurality of computing devices 103 or 106 may beemployed that are arranged, for example, in one or more server banks orcomputer banks or other arrangements. A plurality of computing devices103 or 106 together may comprise, for example, a cloud computingresource, a grid computing resource, and/or any other distributedcomputing arrangement. Such computing devices 103 or 106 may be locatedin a single installation or may be distributed among many differentgeographical locations. For purposes of convenience, the computingdevice 103 or 106 is referred to herein in the singular. Even though thecomputing device 103 or 106 is referred to in the singular, it isunderstood that a plurality of computing devices 103 or 106 may beemployed in the various arrangements as described above.

Various applications and/or other functionality may be executed in thecomputing device 103 according to various embodiments. Also, variousdata is stored in a data store 121 that is accessible to the computingdevice 103. The data store 121 may be representative of a plurality ofdata stores as can be appreciated. The data stored in the data store121, for example, is associated with the operation of the variousapplications and/or functional entities described below.

The components executed on the computing device 103, for example,include a location predictor 124. The location predictor 124 is executedto predict the location of mobile users of client devices 109, andprovide this predicted location to applications executing on a computingdevice 106. The components executed on the computing device 103 may alsoinclude other applications, services, processes, systems, engines, orfunctionality not discussed in detail herein. The data stored in thedata store 121 includes data accessed by the location predictor 124, forexample, recent location data 127, contextual location data 130, andmobile user profiles 133, as well as potentially other data.

The components executed on the computing device 106, for example,include a prediction consumer 136 and a package delivery service 139.The prediction consumer 136 is executed to receive predicted locationsof mobile users from the location predictor 124. The package deliveryservice 139 is executed to arrange package deliveries to mobile users,in cooperation with the location predictor 124 and the predictionconsumer 136.

In various embodiments, the location predictor 124 and the predictionconsumer 136 may utilize any type of middleware framework to communicatewith each other or with an application executing on a client device 109or 115. Examples of such frameworks include remote procedure calls,service-oriented architecture protocol (SOAP), representational statetransfer (REST), Windows Communication Foundation, and other frameworks.

The client device 109 or 115 is representative of a plurality of clientdevices that may be coupled to the network 118. The client device 109 or115 may comprise, for example, a processor-based system such as acomputer system. Such a computer system may be embodied in the form of adesktop computer, a laptop computer, a personal digital assistant, acellular telephone, a portable navigation system, a set-top box, a musicplayer, a video player, a media player, a web pad, a tablet computersystem, a game console, or other devices with like capability.

The client device 109 may be configured to execute various applicationssuch as a location information publisher 142 and/or other applications.The location information publisher 142 may be executed in the clientdevice 109 to provide recent location information and planned futurelocation information to a location predictor 124 executing on thecomputing device 103. The client device 109 may be configured to executeother applications such as, for example, browsers, email applications,instant message applications, navigation applications, and/or otherapplications.

The client device 115 may be configured to execute various applicationssuch as a package delivery client application 145 and/or otherapplications. The package delivery client application 145 may beexecuted in the client device 115 to initiate a package delivery to amobile user, where the mobile user who is the delivery target provideslocation updates through another client device 109. The client device115 may be configured to execute other applications such as, forexample, browsers, email applications, instant message applications,navigation applications, and/or other applications.

Next, a general description of the operation of the various componentsof the networked environment 100 is provided. To begin, a user interactswith the location information publisher 142 to register himself with thelocation predictor 124 executing on the computing device 103. Aregistered user can elect to receive package delivery from one or morepackage delivery agents, where the agent utilizes the package deliveryservice 139 executing on the computing device 106.

At some time after registration, the location information publisher 142sends a recent location of the client device 109 to the locationpredictor 124. The recent location may be provided automatically on aperiodic basis, or the location predictor 124 may query the locationinformation publisher 142. The location predictor 124 stores at least aportion of the received data as recent location data 127.

Another user interacts with the package delivery client application 145executing on the client device 115 to initiate delivery of a package tothe registered mobile user. The package delivery client application 145provides the package delivery service 139 with the identity of themobile user who is the delivery target, and provides details about thepackage, about delivery options, and about the identity of the user whois acting in the role of shipper.

The location predictor 124 uses recent location data 127 for theparticular user, in conjunction with past location information, topredict probable locations of the mobile user of the client device 109at one or more future times. In predicting the probable futurelocation(s), the location predictor 124 may use various other forms ofcontextual location information 130, such as planned future locationsaccording to the user's calendar appointments. This contextual locationinformation 130 may be used instead of, or in addition to, the recentlocation data 127.

The location predictor 124 provides the predicted location(s) to aprediction consumer 136. The prediction consumer 136 in turn providesthe predicted location(s) to the package delivery service 139 executingon the computing device 106. The package delivery service 139 thendetermines a vehicle and a route for that vehicle, and dispatches thevehicle to deliver the package to the registered target user at thepredicted location.

Referring next to FIG. 2, shown is a flowchart that provides one exampleof the operation of a portion of the location predictor 124 according tovarious embodiments. It is understood that the flowchart of FIG. 2provides merely an example of the many different types of functionalarrangements that may be employed to implement the operation of theportion of the location predictor 124 as described herein. As analternative, the flowchart of FIG. 2 may be viewed as depicting anexample of steps of a method implemented in the client device 109(FIG. 1) according to one or more embodiments.

Beginning at box 203, the location predictor 124 receives the recentlocation of the user from the client device 109 associated with theuser. This recent location is provided by the location informationpublisher 142 which is executing on the client device 109. The locationinformation may take the form of global positioning system (GPS)coordinates, a street address (e.g., number, street, and zip code), orany other suitable format. A series of updates as to the recent locationallows the location predictor 124 to track or monitor the user'slocation over time. As updates are received, old information is storedas past location data in contextual location data 130, and the morerecent location is stored as recent location data 127.

Next, at box 206, the location predictor 124 receives a request toprovide a prediction of the user's location at a future time. At box209, the location predictor 124 retrieves past location data as a formof contextual location data 130. At box 212, the location predictor 124uses the past location data in conjunction with the most recent locationto predict the user's location at the requested future time. Theprediction in box 212 uses statistical techniques such as regressionanalysis or other appropriate techniques. Next, at 215 the locationpredictor 124 satisfies the request received in box 206 by providing theprediction to the requester. The process of FIG. 2 is then complete.Under some conditions, the prediction logic in box 212 may determinethat a prediction cannot be made with a predefined level of confidence,and in such cases the location predictor 124 returns an indication tothe requester that no prediction was made.

With reference now to FIG. 3, shown is a flowchart that provides anotherexample of the operation of a portion of the location predictor 124according to various embodiments. It is understood that the flowchart ofFIG. 3 provides merely an example of the many different types offunctional arrangements that may be employed to implement the operationof the portion of the location predictor 124 as described herein. As analternative, the flowchart of FIG. 3 may be viewed as depicting anexample of steps of a method implemented in the client device 109(FIG. 1) according to one or more embodiments.

The embodiments described by the flowchart of FIG. 2 utilize both recentlocation data 127 and past location data, which is a specific form ofcontextual location data 130. The embodiments described by the flowchartof FIG. 3 determine which type of data is available and use one or bothtypes as appropriate. Before discussing the flow of these embodiments,various forms contextual data 130 will be discussed.

One example of contextual location data 130 is information about plannedfuture locations for the user. Planned future locations may include, forexample, appointments or scheduled events obtained from the user'scalendar, trip itineraries obtained from an online trip planning ortravel service, and other types of information about planned futurelocations. Another example of information used in making a prediction isa particular mode of transportation (e.g., car, bus, rail, walking,etc.) that is being used, or is likely to be used, by the mobile user.Current traffic and congestion patterns are examples of otherinformation that can be used by the location predictor 124 to make aprediction about a mobile user's future location.

Another form of contextual data 130 is the type or category of therecent location to predict how long the user will stay at a particularlocation. For example, if the recent location corresponds to arestaurant, the location predictor 124 may predict that the user willstay at that location for one to two hours, and if the recent locationcorresponds to a movie theater, the location predictor 124 may predictthat the user will stay for two to three hours. The location predictor124 may use a geographic information service such as Google® Maps toobtain category information about the recent location.

Another type of contextual location data 130 is the particular time ofday, which can be used in predicting the duration of the mobile user'sstay at a particular location. For example, if the recent location isthe user's office building, the location predictor 124 may predict thatthe user will be present at that location during work hours that areassociated with the particular user. The user profile 133 may be used tostore the user's home location, office location, work hours, etc.

Other types of contextual location data 130 are the recent location andpast location of a proxy user. For example, the user profile 133 maylist one or more proxy users who are associated with the user, forexample, a spouse, a co-worker, etc. The location predictor 124 may uselocation information for the proxy user when, for example, not enoughinformation is available for the actual user to make a confidentprediction. The proxy user's location data may be collected and storedin recent location data 127 and past location data 130 after the proxyuser registers with the location predictor 124. A user may specificallyidentify proxy users. A list of potential proxies may also be identifiedautomatically, for example, by scanning social network websites that theuser has an account with, where these social networks are stored as partof the user profile 133.

Having discussed various examples of contextual location data 130, theoverall flow of FIG. 3 will now be described. Beginning at box 303, thelocation predictor 124 determines whether recent location data 127and/or contextual location data 130 is available. If neither type ofdata is available, the processing for location predictor 124 iscomplete. If, however, it is determined in box 303 that one or both ofthese types of data is available, processing continues at box 306. Atbox 306, the location predictor 124 determines whether both types ofdata (recent location data 127 and contextual location data 130) areavailable. If it is determined at box 306 that both types of data areavailable, processing continues at box 309. At box 309, the locationpredictor 124 makes a prediction of the mobile user's future locationbased on both the recent location data 127 and the contextual locationdata 130. The prediction is made using statistical techniques such asregression analysis, or other appropriate techniques. Processingcontinues next at box 321, which is discussed below.

Returning to box 306, if it is determined at box 306 that one, but notboth, types of data are available, processing continues at box 312. Atbox 312, the location predictor 124 determines whether the type of datathat is available is recent location data 127. If at box 312 it isdetermined that recent location data 127, rather than contextual data130, is available, processing continues at box 315. At box 315, thelocation predictor 124 makes a prediction of the mobile user's futurelocation based on the recent location data 127. The prediction is madeusing statistical techniques such as regression analysis, or otherappropriate techniques. Processing continues next at box 321, which isdiscussed below.

Returning to box 312, if it is determined at box 312 that the type oflocation data that is available is contextual location data 130,processing continues at block 318. At box 318, the location predictor124 makes a prediction of the mobile user's future location based on thecontextual location data 130. The prediction is made using statisticaltechniques such as regression analysis, or other appropriate techniques.Processing continues next at box 321, which is discussed below.

The location predictor 124 moves to box 321 after making a prediction offuture location in box 309, 315, or 318. At box 321, the locationpredictor 124 provides the prediction to a requester or subscriber.Processing for the location predictor 124 is then complete.

Turning now to FIG. 4, shown is a messaging diagram that provides oneexample of how the location predictor 124 interacts with various othercomponents of FIG. 4. It is understood that the diagram of FIG. 4provides merely an example of the many different types of functionalarrangements that may be employed to implement the operation of theportion of the location predictor 124 as described herein. As analternative, the diagram of FIG. 4 may be viewed as depicting an exampleof steps of a method implemented in the computing device 103 (FIG. 1)according to one or more embodiments.

The components shown in FIG. 4 include the location informationpublisher 142, the location predictor 124, the prediction consumer 136,and the package delivery service 139. The scenario shown in FIG. 4begins when the location information publisher 142 registers a mobileuser with the location predictor 124, at step 403. The registrationenables the location information publisher 142 to provide locationupdates for the mobile user to the location predictor 124. Theregistration process may provide the mobile user with various options tocontrol sharing of location updates, such that a mobile user shares theupdates with any interested subscriber, with a subset of subscribers,etc.

At step 406 the mobile user interacts with the package delivery service139 to allow future delivery of packages from one or more packagedelivery agents. Next, at step 409, the package delivery clientapplication 145 communicates with the package delivery service 139 toinitiate a package delivery to a registered mobile user on behalf of apackage sender. The package sender identifies the mobile user who is thedelivery target and provides information about the package to bedelivered. The package sender may also select from various deliverytimes which may differ in price.

Responsive to the initiation of a package delivery to a mobile user atstep 409, the package delivery service 139 notifies the mobile user thata package delivery has been initiated at step 412. At this time, themobile user may notify the package delivery client application 145 thatdelivery is permitted during specific windows of time, or is permittedany time. In response to the package delivery initiation at step 309,the prediction consumer 146 subscribes to updates of the mobile user'spredicted location at step 315.

At step 418 the location information publisher 142 receives one or moreupdates to the mobile user's recent location. The recent locationcoordinates may be obtained from a geographic positioning system (GPS)within the mobile user's client device 109. Alternatively, the recentlocation coordinates may be provided by a GPS system located in avehicle in which the mobile user is traveling. As another option, theclient device 109 may provide a street address and a mapping servicesuch as Google® Maps to translate the street address to GPS coordinates.

At step 421 the location information publisher 142 determines apredicted location of the registered mobile user at a future time andpublishes this information to subscribers such as the predictionconsumer 136. The prediction consumer 136 forwards this information tothe package delivery service 139. At step 424 the package deliveryservice 139 decides that delivery at a particular predicted location andtime is appropriate, and dispatches a vehicle with the package. Thepackage delivery service 139 may use a number of different algorithms tomake this determination and to dispatch an appropriate vehicle. At step424 the package delivery service 139 notifies the mobile user and/or theshipper that delivery is being attempted at a particular time andlocation. In response to the notification of step 424, the mobile usermay provide a more specific update of his predicted location, which isreceived by the location predictor 124 and forwarded to the packagedelivery service 139.

At step 427 the package delivery service 139 notifies the shipper thatdelivery was or was not successful. If the delivery was not successful,the package delivery service 139 may attempt another delivery usingadditional predicted locations provided by the location informationpublisher 142, as the publisher in turn receives recent location updatesfrom the mobile user.

Although the scenario shown in FIG. 4 involves delivery of a singlepackage, the package delivery service 139 may coordinate delivery ofdifferent items. In this manner, items can be delivered at the sametime, within a certain time period, in a particular order, etc.

Although the scenario shown in FIG. 4 involves a single package deliveryservice 139, multiple delivery services may be supported. In such cases,another service or system may coordinate the various delivery services,including keeping track of delivery vehicles from different services. Acoordinating service may provide a mechanism to export delivery data,such that delivery of a particular package may be passed from onedelivery service to another. A pricing system may utilize configurablepricing rules for each delivery service, and may keep track of costs andbilling for each service. The pricing system may provide updated costestimates during the delivery process.

Moving on to FIG. 5, shown is a schematic block diagram of the computingdevice 103 according to an embodiment of the present disclosure. Thecomputing device 103 includes at least one processor circuit, forexample, having a processor 503 and a memory 506, both of which arecoupled to a local interface 509. To this end, the computing device 103may comprise, for example, at least one server computer or like device.The local interface 509 may comprise, for example, a data bus with anaccompanying address/control bus or other bus structure as can beappreciated.

Stored in the memory 506 are both data and several components that areexecutable by the processor 503. In particular, stored in the memory 506and executable by the processor 503 are the location predictor 124, andpotentially other applications. Also stored in the memory 506 may be adata store 121 and other data. In addition, an operating system may bestored in the memory 506 and executable by the processor 503. While notillustrated, the computing device 106 also includes components likethose shown in FIG. 5, whereby the prediction consumer 136 and thepackage delivery service 139 are stored in a memory and executable by aprocessor. Similarly, although not illustrated, the client device 109and the client device 115 also include components like those shown inFIG. 5, where by the package delivery client application 145 and thelocation information publisher 142 are stored in a memory and executableby a processor.

It is understood that there may be other applications that are stored inthe memory 506 and are executable by the processors 503 as can beappreciated. Where any component discussed herein is implemented in theform of software, any one of a number of programming languages may beemployed such as, for example, C, C++, C#, Objective C, Java,Javascript, Perl, PHP, Visual Basic, Python, Ruby, Delphi, Flash, orother programming languages.

A number of software components are stored in the memory 506 and areexecutable by the processor 503. In this respect, the term “executable”means a program file that is in a form that can ultimately be run by theprocessor 503. Examples of executable programs may be, for example, acompiled program that can be translated into machine code in a formatthat can be loaded into a random access portion of the memory 506 andrun by the processor 503, source code that may be expressed in properformat such as object code that is capable of being loaded into a randomaccess portion of the memory 506 and executed by the processor 503, orsource code that may be interpreted by another executable program togenerate instructions in a random access portion of the memory 506 to beexecuted by the processor 503, etc. An executable program may be storedin any portion or component of the memory 506 including, for example,random access memory (RAM), read-only memory (ROM), hard drive,solid-state drive, USB flash drive, memory card, optical disc such ascompact disc (CD) or digital versatile disc (DVD), floppy disk, magnetictape, or other memory components.

The memory 506 is defined herein as including both volatile andnonvolatile memory and data storage components. Volatile components arethose that do not retain data values upon loss of power. Nonvolatilecomponents are those that retain data upon a loss of power. Thus, thememory 506 may comprise, for example, random access memory (RAM),read-only memory (ROM), hard disk drives, solid-state drives, USB flashdrives, memory cards accessed via a memory card reader, floppy disksaccessed via an associated floppy disk drive, optical discs accessed viaan optical disc drive, magnetic tapes accessed via an appropriate tapedrive, and/or other memory components, or a combination of any two ormore of these memory components. In addition, the RAM may comprise, forexample, static random access memory (SRAM), dynamic random accessmemory (DRAM), or magnetic random access memory (MRAM) and other suchdevices. The ROM may comprise, for example, a programmable read-onlymemory (PROM), an erasable programmable read-only memory (EPROM), anelectrically erasable programmable read-only memory (EEPROM), or otherlike memory device.

Also, the processor 503 may represent multiple processors and the memory506 may represent multiple memories that operate in parallel processingcircuits, respectively. In such a case, the local interface 509 may bean appropriate network 112 (FIG. 1) that facilitates communicationbetween any two of the multiple processors 503, between any processor503 and any of the memories 506, or between any two of the memories 506,etc. The local interface 509 may comprise additional systems designed tocoordinate this communication, including, for example, performing loadbalancing. The processor 503 may be of electrical or of some otheravailable construction.

Although the location information publisher 142, the location predictor124, the package delivery service 139, the package delivery clientapplication 145, and other various systems described herein may beembodied in software or code executed by general purpose hardware asdiscussed above, as an alternative the same may also be embodied indedicated hardware or a combination of software/general purpose hardwareand dedicated hardware. If embodied in dedicated hardware, each can beimplemented as a circuit or state machine that employs any one of or acombination of a number of technologies. These technologies may include,but are not limited to, discrete logic circuits having logic gates forimplementing various logic functions upon an application of one or moredata signals, application specific integrated circuits havingappropriate logic gates, or other components, etc. Such technologies aregenerally well known by those skilled in the art and, consequently, arenot described in detail herein.

The diagrams of FIGS. 2, 3, and 4 show the functionality and operationof an implementation of portions of the location information publisher142, the location predictor 124, the package delivery service 139, andthe package delivery client application 145. If embodied in software,each block may represent a module, segment, or portion of code thatcomprises program instructions to implement the specified logicalfunction(s). The program instructions may be embodied in the form ofsource code that comprises human-readable statements written in aprogramming language or machine code that comprises numericalinstructions recognizable by a suitable execution system such as aprocessor 503 in a computer system or other system. The machine code maybe converted from the source code, etc. If embodied in hardware, eachblock may represent a circuit or a number of interconnected circuits toimplement the specified logical function(s).

Although the diagrams of FIGS. 2, 3, and 4 show a specific order ofexecution, it is understood that the order of execution may differ fromthat which is depicted. For example, the order of execution of two ormore blocks may be scrambled relative to the order shown. Also, two ormore blocks shown in succession in FIGS. 2, 3 and 4 may be executedconcurrently or with partial concurrence. Further, in some embodiments,one or more of the blocks shown in FIGS. 2, 3, and 4 may be skipped oromitted. In addition, any number of counters, state variables, warningsemaphores, or messages might be added to the logical flow describedherein, for purposes of enhanced utility, accounting, performancemeasurement, or providing troubleshooting aids, etc. It is understoodthat all such variations are within the scope of the present disclosure.

Also, any logic or application described herein, including locationinformation publisher 142, the location predictor 124, the packagedelivery service 139, and the package delivery client application 145,that comprises software or code can be embodied in any non-transitorycomputer-readable medium for use by or in connection with an instructionexecution system such as, for example, a processor 503 in a computersystem or other system. In this sense, the logic may comprise, forexample, statements including instructions and declarations that can befetched from the computer-readable medium and executed by theinstruction execution system. In the context of the present disclosure,a “computer-readable medium” can be any medium that can contain, store,or maintain the logic or application described herein for use by or inconnection with the instruction execution system. The computer-readablemedium can comprise any one of many physical media such as, for example,magnetic, optical, or semiconductor media. More specific examples of asuitable computer-readable medium would include, but are not limited to,magnetic tapes, magnetic floppy diskettes, magnetic hard drives, memorycards, solid-state drives, USB flash drives, or optical discs. Also, thecomputer-readable medium may be a random access memory (RAM) including,for example, static random access memory (SRAM) and dynamic randomaccess memory (DRAM), or magnetic random access memory (MRAM). Inaddition, the computer-readable medium may be a read-only memory (ROM),a programmable read-only memory (PROM), an erasable programmableread-only memory (EPROM), an electrically erasable programmableread-only memory (EEPROM), or other type of memory device.

It should be emphasized that the above-described embodiments of thepresent disclosure are merely possible examples of implementations setforth for a clear understanding of the principles of the disclosure.Many variations and modifications may be made to the above-describedembodiment(s) without departing substantially from the spirit andprinciples of the disclosure. All such modifications and variations areintended to be included herein within the scope of this disclosure andprotected by the following claims.

Therefore, the following is claimed:
 1. A non-transitorycomputer-readable medium embodying a program executable in a computingdevice, the program comprising: code that receives a registration from amobile user, the registration associated with a package deliveryservice; code that notifies the package delivery service of theregistration; code that receives a recent location for a mobile user;code that receives, from a subscriber, a subscription for updates to therecent location of the mobile user; code that, responsive to receivingthe recent location and the subscription, notifies the subscriber of thereceived recent location of the mobile user; and code that predicts afuture location of the mobile user at a future time based at least inpart on the recent location and on past location data for the mobileuser, the past location data comprising a location update that is olderthan the recent location.
 2. The non-transitory computer-readable mediumof claim 1, wherein the recent location comprises geographic positioningsystem (GPS) coordinates.
 3. The non-transitory computer-readable mediumof claim 1, the program further comprising code that receives an optionfrom the mobile user which controls sharing of the updates to the recentlocation.
 4. The non-transitory computer-readable medium of claim 1,wherein the code that predicts also bases a prediction on informationabout planned future locations of the mobile user.
 5. A system,comprising: at least one computing device; and a location predictorexecutable in the at least one computing device, the location predictorcomprising: logic that receives a recent location of a mobile user; andlogic that predicts a future location of the mobile user at a futuretime based at least in part on the recent location, on past locationdata for the mobile user and on information about planned futurelocations of the mobile user, wherein the past location data comprises alocation update that is older than the recent location.
 6. The system ofclaim 5, wherein the logic that predicts the future location also basesa prediction on a category of the recent location.
 7. The system ofclaim 5, wherein the logic that predicts the future location also basesa prediction on a current time of day.
 8. The system of claim 5, whereinthe logic that predicts the future location also bases a prediction onprofile information for the mobile user.
 9. The system of claim 5,wherein the logic that predicts the future location also bases aprediction on location information for a proxy user.
 10. The system ofclaim 5, wherein the recent location comprises geographic positioningsystem (GPS) coordinates.
 11. The system of claim 5, wherein the recentlocation comprises a street address.
 12. A method, comprising the stepsof: determining availability of recent location data for a mobile user;determining availability of contextual location data for the mobileuser; and predicting a future location of the mobile user at a futuretime based on at least one of recent location data and contextuallocation data, depending on the availability of the recent location dataand the contextual location data, wherein the contextual location dataincludes planned future locations of the mobile user.
 13. The method ofclaim 12, further comprising the steps of: receiving a query for thepredicted future location; and responsive to the query, providing thepredicted future location.
 14. The method of claim 12, furthercomprising the step of receiving a query for the predicted futurelocation, wherein the predicting is responsive to the query.
 15. Themethod of claim 12, further comprising the steps of: receiving arequest, from a subscriber, to subscribe to predictions; and responsiveto completing a prediction of the future location, providing thepredicted future location to the subscriber.
 16. The method of claim 12,wherein the contextual location data includes a category of the recentlocation data.
 17. The method of claim 12, wherein the contextuallocation data includes a current time of day.
 18. The method of claim12, wherein the contextual location data includes location informationfor a proxy user.